Main Road Extraction From ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

PLoS One. 2015 Sep 23;10(9):e0138071. doi: 10.1371/journal.pone.0138071. eCollection 2015.


Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cities*
  • Geographic Information Systems*
  • Image Processing, Computer-Assisted*
  • Satellite Communications*

Grant support

This work was supported by the National Natural Science Foundation of China (No. 41201395, 41161069,41271399), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120141110036), Science and Technology Project of Jiangxi Provincial Education Department (No. GJJ14479), and the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (WE2015011).